{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JCD7LUCNL723UKWMMEODKDSHKP","short_pith_number":"pith:JCD7LUCN","schema_version":"1.0","canonical_sha256":"4887f5d04d5ff5ba2acc611c350e4753dccbee544404073d291ab3581fc56f8e","source":{"kind":"arxiv","id":"2606.07313","version":1},"attestation_state":"computed","paper":{"title":"SV-Detect: AI-generated Text Detection with Steering Vectors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Mikhail Vishnyakov, Tatiana Gaintseva","submitted_at":"2026-06-05T14:34:37Z","abstract_excerpt":"Detecting machine-generated text is especially difficult under distribution shift, such as transfer across domains, source models, and editing attacks. We propose a fake-text detector based on steering vectors extracted from the hidden representations of a frozen language model. At each layer, we construct a direction that separates human-written from machine-generated text, and represent each input by its layer-wise alignment with these directions. A lightweight classifier trained on these projection features yields the final detection score. Our method achieves strong performance both in-dis"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.07313","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-05T14:34:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7bea683337f4c2163010f966dee066c7c4ad179e64908dcfdcee411fe4310056","abstract_canon_sha256":"592ea748b50c88c37a8cf34f2daa43e7e5a8190ce7bb0b7d94e97b433f006f19"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:05:19.097527Z","signature_b64":"kQkwo9yHYb7SwynUuNraUMd5r9pO4fyQ81kiWO234rUHbQDf57Frk5UU/wiabrPEhemJde5sElBGdv5p7kAsCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4887f5d04d5ff5ba2acc611c350e4753dccbee544404073d291ab3581fc56f8e","last_reissued_at":"2026-06-08T01:05:19.096690Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:05:19.096690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SV-Detect: AI-generated Text Detection with Steering Vectors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Mikhail Vishnyakov, Tatiana Gaintseva","submitted_at":"2026-06-05T14:34:37Z","abstract_excerpt":"Detecting machine-generated text is especially difficult under distribution shift, such as transfer across domains, source models, and editing attacks. We propose a fake-text detector based on steering vectors extracted from the hidden representations of a frozen language model. At each layer, we construct a direction that separates human-written from machine-generated text, and represent each input by its layer-wise alignment with these directions. A lightweight classifier trained on these projection features yields the final detection score. Our method achieves strong performance both in-dis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07313","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.07313/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.07313","created_at":"2026-06-08T01:05:19.096824+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.07313v1","created_at":"2026-06-08T01:05:19.096824+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07313","created_at":"2026-06-08T01:05:19.096824+00:00"},{"alias_kind":"pith_short_12","alias_value":"JCD7LUCNL723","created_at":"2026-06-08T01:05:19.096824+00:00"},{"alias_kind":"pith_short_16","alias_value":"JCD7LUCNL723UKWM","created_at":"2026-06-08T01:05:19.096824+00:00"},{"alias_kind":"pith_short_8","alias_value":"JCD7LUCN","created_at":"2026-06-08T01:05:19.096824+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JCD7LUCNL723UKWMMEODKDSHKP","json":"https://pith.science/pith/JCD7LUCNL723UKWMMEODKDSHKP.json","graph_json":"https://pith.science/api/pith-number/JCD7LUCNL723UKWMMEODKDSHKP/graph.json","events_json":"https://pith.science/api/pith-number/JCD7LUCNL723UKWMMEODKDSHKP/events.json","paper":"https://pith.science/paper/JCD7LUCN"},"agent_actions":{"view_html":"https://pith.science/pith/JCD7LUCNL723UKWMMEODKDSHKP","download_json":"https://pith.science/pith/JCD7LUCNL723UKWMMEODKDSHKP.json","view_paper":"https://pith.science/paper/JCD7LUCN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.07313&json=true","fetch_graph":"https://pith.science/api/pith-number/JCD7LUCNL723UKWMMEODKDSHKP/graph.json","fetch_events":"https://pith.science/api/pith-number/JCD7LUCNL723UKWMMEODKDSHKP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JCD7LUCNL723UKWMMEODKDSHKP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JCD7LUCNL723UKWMMEODKDSHKP/action/storage_attestation","attest_author":"https://pith.science/pith/JCD7LUCNL723UKWMMEODKDSHKP/action/author_attestation","sign_citation":"https://pith.science/pith/JCD7LUCNL723UKWMMEODKDSHKP/action/citation_signature","submit_replication":"https://pith.science/pith/JCD7LUCNL723UKWMMEODKDSHKP/action/replication_record"}},"created_at":"2026-06-08T01:05:19.096824+00:00","updated_at":"2026-06-08T01:05:19.096824+00:00"}